Aims and objectives
In the current version of the prostata imaging data and reporting system (PI-RADS version 2),
the role of dynamic contrast-enhanced MRI (DCE-MRI) is secondary to diffusion imaging and it focuses on subjectively (qualitatively) assessing the enhancement characteristics of the peripheral zone. In this work we aim to further investigate the capability of DCE-MRI perfusion imaging biomarkers to predict prostate tumor aggressiveness by using multivariate image analysis techniques,
Methods and materials
In this IRB-approved retrospective study,30 histologically confirmed peripheral prostate tumors were studied with 3T MRI,
including DCE-MR series with high temporal resolution.
Tumor ROIs were manually segmented in areas with pathologic confirmation (15 Gleason-6 and 15 Gleason-7) as delimitated on the T2-weighted/Diffusion-weighted images. Pharmacokinetic parameters from first (extended Tofts) and second (2-compartment exchange,
adiabatic approximation to tissue homogeneity and distributed...
PLS-DA global model was able to correctly predict 81% of the cases,
while the best individual model (Extended Tofts) obtained 71% of correct predictions (Figures 2 and 3).
DCE-MRI pharmacokinetic parameters and latent variable extraction provide additional and valuable information to characterize and predict tumor aggressiveness in prostate cancer.
The global model outperforms the individual parameters by taking advantage of the combination of different sources of independent information to obtain better predictions.
Contact information: Roberto Sanz-Requena email@example.com
Prats-Montalbán JM et al.
Prostate functional magnetic resonance image analysis using multivariate curve resolution methods.
J Chemometrics; 2014:28:672-680. Aguado-Sarrió E et al.
Prostate diffusion weighted-magnetic resonance imaging analysis using multivariate curve resolution methods.
Chemolab 2015;140:43-48. Sanz-Requena R et al.
Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of...